🌌 Reshaped #4
Data trusts, Twitter stories, CRISPR achievements, startup failures, South Korean composting and much more
Welcome to a new issue of Reshaped, a newsletter for those who do not want to miss a thing about the huge transformations of our time.
It was an interesting week for tech business, so expect some good news. As promised, I will deepen the value of data, which is strictly related to their openness, and introduce the concept of data trust.
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News on tech innovation
✈️ The cancellation of some of the main tech conferences around the world due to coronavirus has already caused losses for $666 million. The estimation only takes into account airfare, lodging, food, and transportation - missed revenues from the events themselves are not included in the calculation (Vox). Prevention goes beyond mere event cancellation, as Silicon Valley is organizing to reduce travels and meetings (The Verge).
🗽 U.S. Attorney General William Barr has centralized control over D.O.J.’s antitrust division to the detriment of Assistant Attorney General Makan Delrahim. This is a piece of bad news for Big Tech companies, as stronger antitrust enforcement is to be expected in the next months under the new leadership. The relationship between Mr. Barr and Mr. Delrahim has never been easy, due to different views on U.S. tech giants and M&A policy (Politico).
🥡 DoorDash, the leading food delivery startup in the U.S., has confidentially filed to go public. The company, financed by SoftBank, is losing money ($450 million in 2019 according to anonymous sources) and is facing strong complaints from regulators, competitors, and traditional market players. In a difficult period for stock markets, due to coronavirus outbreak and tech skepticism, CEO Tony Xu wants to capitalize on the $12.7 billion valuation of the company and bet on future profitability (The New York Times).
🐤 After the strategic acquisition of the startup Chroma Labs, Twitter will test a new type of tweets that will last only 24 hours, called “fleets”. Fleets will be tested in Brazil (The Verge). In the meantime, CEO and co-founder Jack Dorsey is at risk of losing his job, as stockholder Elliott Management intends to replace him (The New York Times).
🧬 CRISPR has been tested for the first time directly into the body of a patient affected by genetic blindness (Nature).
💲 The European Commission wants Amazon to pay $280 million in back taxes to Luxembourg because of transfer pricing issues (International Tax Review).
🎩 Nokia will replace CEO Rajeev Suri with Pekka Lundmark in September, due to the increasing gap with Huawei and Ericsson in the 5G technology (Bloomberg). The company has signed new partnerships with Intel and Marvell over 5G chips (Techradar).
🚗 Alphabet-owned autonomous vehicle startup Waymo has raised $2.25 billion in its first funding backed by external investors, a sign that the company is ready to invest heavily in R&D to spin out of the parent company (Forbes).
🏷 The value of data
In the last issues, I briefly introduced some aspects of the data economy, explaining the limits of two common definitions of data as oil (private good) or sunlight (public good). Being non-rivalrous and excludable, data seem to fall entirely in the definition of club good: goods that can be used by everyone without risks of deterioration, but that can also be limited by owners. This view is intermediate between closed and open data and can be defined as shared data. The following graph, elaborated by the Open Data Institute, illustrates the three main categories of data openness.
The openness of data is strictly related to their value. Open data can be used by a wide range of actors to create new products and services, improve artificial intelligence algorithms, address social issues, and monitor policy implementation. However, the economics of data make it difficult to broaden access to data, as reported by a new research paper by the Bennett Institute for Public Policy.
Because data is non-rival and has externalities, the market is unlikely to provide the best overall outcomes. Value comes from data being brought together, and that requires organisations to let others use the data they hold. But if they do, organisations will not get all the benefits from data they have collected, and perhaps not enough benefits to cover the cost of collecting and storing the data in the first place.
This translates into three important tradeoffs. First, we need incentives to invest in data-driven business models to overcome negative market effects. Second, we need to address the issue of personally and commercially sensitive data, which reduces the chances to open private databases. Third, we need to provide evidence of how investments in data can generate revenues and justify high costs. Ideally, solving those tradeoffs should improve communication between private and public actors in data sharing in a way that maximizes value without any ethical or legal concern, by removing barriers such as technology, licensing, and costs.
A decisive step towards this scenario is the creation of a solid and trustworthy institutional and regulatory environment capable of simplifying regulation and promoting data access schemes. Data trusts are an example of an institution that is gaining momentum in that direction by reducing information asymmetry in the data economy. As defined by Anouk Ruhaak,
A data trust is a structure whereby data is placed under the control of a board of trustees with a fiduciary responsibility to look after the interests of the beneficiaries — you, me, society. Using them offers all of us the chance of a greater say in how our data is collected, accessed and used by others. This goes further than limiting data collecting and access to protect our privacy; it promotes the beneficial use of data, and ensures these benefits are widely felt across society.
The Bennett Institute report illustrates some other benefits of a data trust.
Trustees of a data trust may need to have powers strong enough to discourage misuse of the data, in line with Ostrom’s principles. Data trusts may be able to reduce transaction costs and increase efficiency, by allowing one data sharing agreement between partners rather than their having to negotiate several. They may be able to set conditions for the quality of data provided by members, perhaps reducing information asymmetries. Data trusts may also be a way to compensate for ‘missing markets’.
Whatever the form, data as an infrastructure need regulation because markets tend to be unable to generate the best economic and social outcome. The value of data produced by governments, private organizations, and individuals can be unlocked only if we manage to create the best balance in a sharing data model. Data trusts are starting to be implemented for smart city projects around the world and have the potential to impact positively how we make use of data both at private and public levels. Will they stand a chance against data economy behemoths?
Alternative perspectives
🌠 Erin Griffith from The New York Times highlights the recent layoff trend in the startup environment, with big drops in investments and failed IPOs. This trend, however, cannot be generalized to the entire sector, as heavily influenced by what is now called the “SoftBank effect”. Startups that rely heavily upon physical assets and workforce find it hard to balance high expenses with enough revenues, thus leading to chronic unprofitability.
It’s a humbling shift for an industry that long saw itself as an engine of job creation and innovation, producing the ride-hailing giant Uber, the hospitality company Airbnb and other now well-known brands that often disrupted entrenched industries. Their rise was propelled by a wave of investor money — about $763 billion washed into start-ups in the United States over the last decade — that also fueled the growth of young companies in delivery, cannabis, real estate and direct-to-consumer goods.
Investors are starting to pretend a faster path towards profitability, which is fundamental for a healthy startup ecosystem. The end of the WeWork model can help to restore confidence among investors and policymakers.
🚨 Maya MacGuineas, president of the U.S. Committee for a Responsible Federal Budget, thinks that technology could represent a serious threat to capitalism. In an article on The Atlantic, she claims that tech companies have surpassed the typical gain-and-retain business model. They now aspire to create an addiction to hook and engage users. The problem, according to the author, is that this mechanism could distort the capability of individuals to act according to their self-interest, which is the basis of capitalism and the free market.
The suggestion that we need to be protected from such tactics might seem paternalistic, and if consumers were the rational actors who populate econ textbooks, it might be: A person could decide for herself whether to exchange some amount of privacy for the joy of viewing friends’ photos or the convenience of tracking her heart rate. But the addiction economy relies on an asymmetrical exchange of information. Users are expected to blithely surrender their private information for access to services. The data collectors, meanwhile, fiercely guard their own privacy, typically refusing to disclose what information they have, whom they sell it to, and how they use it to manipulate our behavior.
Other readings
🧺 In the last issue of The New Yorker, Rivka Galchen explores how South Korea is addressing urban sustainability through composting.
⭐️ Timothy Garton Ash from The Guardian argues that the E.U. should pursue its natural superpower ambitions without huge institutional changes, which are difficult to agree on and hard to implement. Instead, he believes Europe should act in harmony by coordinating crucial political actions.
💥 Sooraj Shah briefly analyzes why artificial intelligence has not changed the world yet (BBC).
🛴 David Corn from Mother Jones investigates how Gavril Yushvaev, a Russian billionaire with a criminal past, managed to become a major investor in some U.S. tech companies, including Lyft.
Thanks for reading.
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Have a good weekend!
Federico